SPIN Unprocessed October 23, 2026 ai_technology research
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
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arXiv:2610.28698v1 Announce Type: new Abstract: Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these gains extend to virtual worlds with different visual styles, dynamics, controls, and objectives remains unclear. We investigate this question through a controlled study of continued pre-training (CPT) across three axes: data volume, diversity, and model size. Our 120 tra
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